Prospective Study on Plasma MicroRNA‐4286 and Incident Acute Coronary Syndrome

Background Mounting evidence suggests that circulating microRNAs (miRNAs) are critical indicators of cardiovascular disease. However, prospective studies linking circulating miRNAs to incident acute coronary syndrome (ACS) are limited, and the underlying effect of associated miRNA on incident ACS remains unknown. Methods and Results Based on a 2‐stage prospective nested case–control design within the Dongfeng‐Tongji cohort, we profiled plasma miRNAs from 23 pairs of incident ACS cases and controls by microarray and validated the candidate miRNAs in 572 incident ACS case–control pairs using quantitative real‐time polymerase chain reaction. We observed that plasma miR‐4286 was associated with higher risk of ACS (adjusted odds ratio according to an interquartile range increase, 1.26 [95% CI, 1.07–1.48]). Further association analysis revealed that triglyceride was positively associated with plasma miR‐4286, and an interquartile range increase in triglyceride was associated with an 11.04% (95% CI, 3.77%–18.83%) increase in plasma miR‐4286. In addition, the Mendelian randomization analysis suggested a potential causal effect of triglyceride on plasma miR‐4286 ( β coefficients: 0.27 [95% CI, 0.01–0.53] and 0.27 [95% CI, 0.07–0.47] separately by inverse variance‐weighted and Mendelian randomization‐pleiotropy residual sum and outlier tests). Moreover, the causal mediation analysis indicated that plasma miR‐4286 explained 5.5% (95% CI, 0.7%–17.0%) of the association of triglyceride with incident ACS. Conclusions Higher level of plasma miR‐4286 was associated with an increased risk of ACS. The upregulated miR‐4286 in plasma can be attributed to higher triglyceride level and may mediate the effect of triglyceride on incident ACS.

Interest rate risk in the banking book – is the SSM’s regulatory approach tight enough?

The current low interest rate environment is an unprecedented situation for the European banking union’s single supervisory mechanism (SSM) in that it increases interest rate risk in the banking book (IRRBB) for euro area banks. Sudden upward movements in rates threaten the economic value of bank equity, and persistently high interest rates can lead to lower bank earnings. These risks point to the need for a comprehensive supervisory approach to regulating IRRBB. Given the extraordinary circumstances and high levels of IRRBB which banks are and will be exposed to, we evaluate whether the SSM’s regulatory approach is tight enough. Specifically, we assess the adequacy of the supervisory outlier tests by performing an empirical analysis on historical interest rate changes and discussing whether the earnings perspective should be included in the supervisory outlier tests. Furthermore, we consider the minimum capital requirements for IRRBB against the background of the current low interest rates. Overall, we conclude that the current SSM’s approach on IRRBB is not tight enough. While we confirm the adequacy of the existing supervisory outlier tests, we recommend complementing them with outlier tests regarding the net interest income of banks. We further recommend implementing a standardised approach for calculating minimum capital requirements to improve banks’ resilience against IRRBB.

Evaluation of the minimum sampling design for population genomic and microsatellite studies. An analysis based on wild maize

ABSTRACTMassive parallel sequencing is revolutionizing the field of molecular ecology by allowing to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the needed economic and computational resources generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two extensive genomic and one large microsatellite datasets consisting of empirical data. We generated different subsampling designs by changing the number of loci, individuals, populations and individuals per population to test for deviations in classic population genetics parameters (HS, FIS, FST) and landscape genetic tests (isolation by distance and environment, central abundance hypothesis). We also tested the effect of sampling different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the genomic and microsatellite datasets. For the genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For all datasets we found that increasing the number of population sampled is important to obtain precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.

Environment Monitoring for Anomaly Detection System Using Smartphones

Currently, the popularity of smartphones with networking capabilities equipped with various sensors and the low cost of the Internet have opened up great opportunities for the use of smartphones for sensing systems. One of the most popular applications is the monitoring and the detection of anomalies in the environment. In this article, we propose to enhance classic road anomaly detection methods using the Grubbs test on a sliding window to make it adaptive to the local characteristics of the road. This allows more precision in the detection of potholes and also building algorithms that consume less resources on smartphones and adapt better to real conditions by applying statistical outlier tests on current threshold-based anomaly detection methods. We also include a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Experiments and simulations allow us to confirm the effectiveness of the proposed methods.

Comparative Genome-Wide Survey of Single Nucleotide Variation Uncovers the Genetic Diversity and Potential Biomedical Applications among Six Macaca Species

Macaca is of great importance in evolutionary and biomedical research. Aiming at elucidating genetic diversity patterns and potential biomedical applications of macaques, we characterized single nucleotide variations (SNVs) of six Macaca species based on the reference genome of Macaca mulatta. Using eight whole-genome sequences, representing the most comprehensive genomic SNV study in Macaca to date, we focused on discovery and comparison of nonsynonymous SNVs (nsSNVs) with bioinformatic tools. We observed that SNV distribution patterns were generally congruent among the eight individuals. Outlier tests of nsSNV distribution patterns detected 319 bins with significantly distinct genetic divergence among macaques, including differences in genes associated with taste transduction, homologous recombination, and fat and protein digestion. Genes with specific nsSNVs in various macaques were differentially enriched for metabolism pathways, such as glycolysis, protein digestion and absorption. On average, 24.95% and 11.67% specific nsSNVs were putatively deleterious according to PolyPhen2 and SIFT4G, respectively, among which the shared deleterious SNVs were located in 564–1981 genes. These genes displayed enrichment signals in the ‘obesity-related traits’ disease category for all surveyed macaques, confirming that they were suitable models for obesity related studies. Additional enriched disease categories were observed in some macaques, exhibiting promising potential for biomedical application. Positively selected genes identified by PAML in most tested Macaca species played roles in immune and nervous system, growth and development, and fat metabolism. We propose that metabolism and body size play important roles in the evolutionary adaptation of macaques.

Rapid discovery of SNPs that differentiate hatchery steelhead trout from ESA-listed natural-origin steelhead trout using a 57K SNP array

Natural-origin steelhead trout (Oncorhynchus mykiss (Walbaum, 1792)) in the Pacific Northwest, USA, are threatened by a number of factors including habitat destruction, disease, decline in marine survival, and a potential erosion of genetic viability due to introgression from hatchery strains. Our major goal was to use a recently developed SNP array containing ∼57 000 SNPs to identify a subset of SNPs that differentiate hatchery and natural-origin populations. We analyzed 35 765 polymorphic SNPs in nine populations of steelhead trout sampled from Puget Sound, Washington, USA. We then conducted two outlier tests and found 360 loci that were candidates for divergent selection between hatchery and natural-origin populations (mean FCT = 0.29, maximum = 0.65) and 595 SNPs that were candidates for selection among natural-origin populations (mean FST = 0.25, maximum = 0.51). Comparisons with a linkage map revealed that two chromosomes (Omy05 and Omy25) contained significantly more outliers than other chromosomes, suggesting that regions on Omy05 and Omy25 may be of adaptive significance. Our results highlight several advantages of the 57 000 SNP array as a tool for population and conservation genomics studies.